Our model builds a panoptic radiance field representation of any scene from just color images.
We present NeSF, a method for producing 3D semantic fields from posed RGB images alone.
With the recent growth of urban mapping and autonomous driving efforts, there has been an explosion of raw 3D data collected from terrestrial platforms with lidar scanners and color cameras.
To the best of our knowledge, MDIF is the first deep implicit function model that can at the same time (1) represent different levels of detail and allow progressive decoding; (2) support both encoder-decoder inference and decoder-only latent optimization, and fulfill multiple applications; (3) perform detailed decoder-only shape completion.
We present a method for differentiable rendering of 3D surfaces that supports both explicit and implicit representations, provides derivatives at occlusion boundaries, and is fast and simple to implement.
Unlike neural scene representation work that optimizes per-scene functions for rendering, we learn a generic view interpolation function that generalizes to novel scenes.
The goal of this project is to learn a 3D shape representation that enables accurate surface reconstruction, compact storage, efficient computation, consistency for similar shapes, generalization across diverse shape categories, and inference from depth camera observations.
We design a simple but surprisingly effective visual recognition benchmark for studying bias mitigation.
To edit a video, the user has to only edit the transcript, and an optimization strategy then chooses segments of the input corpus as base material.
To allow for widely varying geometry and topology, we choose an implicit surface representation based on composition of local shape elements.
We train a regression network using these objectives, a set of unlabeled photographs, and the morphable model itself, and demonstrate state-of-the-art results.
Ranked #2 on 3D Face Reconstruction on Florence (Average 3D Error metric)
We provide a search algorithm that generates a sampling of likely candidate views according to the example distribution, and a set selection algorithm that chooses a subset of the candidates that jointly cover the example distribution.